Recovering Bathymetry From Satellite Altimetry-Derived Gravity by Fully Connected Deep Neural Network
文献类型：期刊论文
| 作者 | Yang, Lei1,2,3; Liu, Min4; Liu, Na1; Guo, Jinyun5; Lin, Lina1; Zhang, Yuyuan1; Du, Xing1; Xu, Yongsheng2 ; Zhu, Chengcheng6; Wang, Yongkang7
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| 刊名 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
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| 出版日期 | 2023 |
| 卷号 | 20页码:5 |
| 关键词 | Gravity Bathymetry Satellites Geology Altimetry Geologic measurements Sea measurements convolutional neural network (CNN) fully connected deep neural network (FC-DNN) gravity gravity-geological method satellite altimetry |
| ISSN号 | 1545-598X |
| DOI | 10.1109/LGRS.2023.3302992 |
| 通讯作者 | Liu, Min(unhackor@163.com) |
| 英文摘要 | The topography of the seafloor is highly correlated with the local gravity through intrinsically nonlinear relationships across a particular wavelength band. The purpose of this study is to compare a fully connected deep neural network (FC-DNN) and a convolutional neural network (CNN) with the gravity-geological method (GGM) to determine whether deep learning can provide superior predictions of bathymetry. We include the short-wavelength gravity (SG) and geological models as training parameters, and assess the performance of different models and parameter combinations using various inputs. Compared with the CNN method, the FC-DNN with the SG as an input reduces the standard deviation (STD) of bathymetry differences from 118.6 m to about 73.5 m. The FC-DNN with SG reduces the STD of bathymetry differences by up to 13.3% compared with the conventional GGM. Furthermore, we demonstrate that the addition of geological information alongside the SG does not significantly enhance the accuracy. Power spectral density analysis suggests that the FC-DNN is superior for predicting wavelengths shorter than 6 km. |
| WOS关键词 | MODEL |
| 资助项目 | National Natural Science Foundation of China[41806214] ; National Natural Science Foundation of China[42106232] |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001063563700007 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/181817]  |
| 专题 | 海洋研究所_海洋环流与波动重点实验室 |
| 通讯作者 | Liu, Min |
| 作者单位 | 1.Minist Nat Resources, Inst Oceanog 1, Qingdao 266061, Peoples R China 2.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao 266071, Shandong, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100864, Peoples R China 4.91001 Unit, Beijing 100841, Peoples R China 5.Shandong Univ Sci & Technol, Coll Geodesy & Geomatics, Qingdao, Peoples R China 6.Shandong Jianzhu Univ, Sch Surveying & Geoinformat, Jinan 250101, Peoples R China 7.Natl Marine Environm Monitoring Ctr, Dalian 116023, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Lei,Liu, Min,Liu, Na,et al. Recovering Bathymetry From Satellite Altimetry-Derived Gravity by Fully Connected Deep Neural Network[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2023,20:5. |
| APA | Yang, Lei.,Liu, Min.,Liu, Na.,Guo, Jinyun.,Lin, Lina.,...&Wang, Yongkang.(2023).Recovering Bathymetry From Satellite Altimetry-Derived Gravity by Fully Connected Deep Neural Network.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,20,5. |
| MLA | Yang, Lei,et al."Recovering Bathymetry From Satellite Altimetry-Derived Gravity by Fully Connected Deep Neural Network".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 20(2023):5. |
入库方式： OAI收割
来源：海洋研究所
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